Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Classifier

In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.


Step 0: Load The Data

In [1]:
# Load pickled data
import pickle
import time
import random
import numpy as np
import csv
import tensorflow as tf
from tensorflow.python.client import device_lib
from tensorflow.contrib.layers import flatten
from sklearn.utils import shuffle
from sklearn import preprocessing
from skimage import exposure
import cv2
import pandas as pd
from matplotlib import gridspec
import matplotlib.pyplot as plt
%matplotlib inline

import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos]

print("Available computing units: ", get_available_gpus())


training_file   = "../train.p"
validation_file = "../valid.p"
testing_file    = "../test.p" 
labels_file     = "signnames.csv"

colorspace = "BW"  # ["RGB", "YUV", "LAB", "BW"]
show_signs = False

with open(training_file, mode='rb') as f:
    train = pickle.load(f)
with open(validation_file, mode='rb') as f:
    valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
    test = pickle.load(f)

X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test,  y_test  = test['features'],  test['labels']

# check if we have equal amount of samples in the feature and label spaces
assert(len(X_train) == len(y_train))
assert(len(X_valid) == len(y_valid))
assert(len(X_test) == len(y_test))

# Read in label descriptions
labels = []
with open(labels_file, 'r') as csvfile:
    spamreader = csv.reader(csvfile, delimiter=',')
    for row in spamreader:
        if row[0].isdecimal():
            labels.append(row[1])
Using TensorFlow backend.
Available computing units:  ['/device:CPU:0']

Step 1: Dataset Summary & Exploration

The pickled data is a dictionary with 4 key/value pairs:

  • 'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).
  • 'labels' is a 1D array containing the label/class id of the traffic sign. The file signnames.csv contains id -> name mappings for each id.
  • 'sizes' is a list containing tuples, (width, height) representing the original width and height the image.
  • 'coords' is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES

Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.

Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas

In [2]:
n_train       = X_train.shape[0]
n_validation  = X_valid.shape[0]
n_test        = X_test.shape[0]
image_shape   = X_train.shape[1:]
n_classes     = np.unique(y_train).size

n_total = n_train + n_validation + n_test
print("Training dataset size:    %d (%.2f%%) samples" % (n_train, 100*n_train/n_total))
print("Validation dataset size:  %d (%.2f%%) samples" % (n_validation, 100*n_validation/n_total))
print("Test dataset size:        %d (%.2f%%) samples" % (n_test, 100*n_test/n_total))
print("Image data shape:        ", image_shape)
print("Number of classes:       ", n_classes)
Training dataset size:    34799 (67.13%) samples
Validation dataset size:  4410 (8.51%) samples
Test dataset size:        12630 (24.36%) samples
Image data shape:         (32, 32, 3)
Number of classes:        43

Include an exploratory visualization of the dataset

Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.

The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.

NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?

In [3]:
# Check how many traffic sign example we have in each class within the training dataset
class_indexes_train = []
class_indexes_valid = []
class_indexes_test  = []
for i in range(n_classes):
    class_indexes_train.append(np.where(y_train==i))
    class_indexes_valid.append(np.where(y_valid==i))
    class_indexes_test.append(np.where(y_test==i))

df = pd.DataFrame({
    'Traffic sign description': labels,
    'Training'   : [len(class_indexes_train[i][0]) for i in range(n_classes)],
    'Validation' : [len(class_indexes_valid[i][0]) for i in range(n_classes)],
    'Test'       : [len(class_indexes_test[i][0])  for i in range(n_classes)]
})

pd.options.display.width = 100
pd.options.display.max_colwidth = 90
df.reindex(columns=['Traffic sign description', 'Training', 'Validation', 'Test'])
Out[3]:
Traffic sign description Training Validation Test
0 Speed limit (20km/h) 180 30 60
1 Speed limit (30km/h) 1980 240 720
2 Speed limit (50km/h) 2010 240 750
3 Speed limit (60km/h) 1260 150 450
4 Speed limit (70km/h) 1770 210 660
5 Speed limit (80km/h) 1650 210 630
6 End of speed limit (80km/h) 360 60 150
7 Speed limit (100km/h) 1290 150 450
8 Speed limit (120km/h) 1260 150 450
9 No passing 1320 150 480
10 No passing for vehicles over 3.5 metric tons 1800 210 660
11 Right-of-way at the next intersection 1170 150 420
12 Priority road 1890 210 690
13 Yield 1920 240 720
14 Stop 690 90 270
15 No vehicles 540 90 210
16 Vehicles over 3.5 metric tons prohibited 360 60 150
17 No entry 990 120 360
18 General caution 1080 120 390
19 Dangerous curve to the left 180 30 60
20 Dangerous curve to the right 300 60 90
21 Double curve 270 60 90
22 Bumpy road 330 60 120
23 Slippery road 450 60 150
24 Road narrows on the right 240 30 90
25 Road work 1350 150 480
26 Traffic signals 540 60 180
27 Pedestrians 210 30 60
28 Children crossing 480 60 150
29 Bicycles crossing 240 30 90
30 Beware of ice/snow 390 60 150
31 Wild animals crossing 690 90 270
32 End of all speed and passing limits 210 30 60
33 Turn right ahead 599 90 210
34 Turn left ahead 360 60 120
35 Ahead only 1080 120 390
36 Go straight or right 330 60 120
37 Go straight or left 180 30 60
38 Keep right 1860 210 690
39 Keep left 270 30 90
40 Roundabout mandatory 300 60 90
41 End of no passing 210 30 60
42 End of no passing by vehicles over 3.5 metric tons 210 30 90
In [4]:
def visualize_dataset(data, indexes, imgs_per_class=10):
    #norm = colors.LogNorm(X_mean + 0.5 * X_std, 1.0, clip='True')
    #norm = colors.LogNorm(vmin=-1.0, vmax=1.0)
    #norm = colors.LogNorm(image.mean() + 0.5 * image.std(), image.max(), clip='True')
    print("min val: %f, max val: %f" % (np.min(data), np.max(data)))
    print("mean: %f, std: %f" %(data.mean(), data.std()))
    data_conv = np.copy(data)

    imshow_cmap = None
    if data.shape[-1] == 1:  # or colorspace == "BW"
        imshow_cmap = "gray"

    if colorspace == "YUV":
        for i, img in enumerate(data):
            data_conv[i] = np.reshape(cv2.cvtColor(img, cv2.COLOR_YUV2RGB), data.shape[1:])
    elif colorspace == "LAB":
        for i, img in enumerate(data):
            data_conv[i] = np.reshape(cv2.cvtColor(img, cv2.COLOR_Lab2RGB), data.shape[1:])
   
    for sign_class in range(n_classes):
        print("Showing example images from class %d: %s" % (sign_class, labels[sign_class]))
        plt.figure(figsize=(10,3))
        rand_items = random.sample(list(indexes[sign_class][0]), imgs_per_class)
        for count, i in zip(range(imgs_per_class), rand_items):
            image = data_conv[i].squeeze()
            ax = plt.subplot(1, imgs_per_class, count+1)
            ax.set_title(i, fontdict={'fontsize': 8})
            ax.set_axis_off()
            ax.imshow(image, cmap=imshow_cmap) #, vmin=-1.0, vmax=1.0, cmap="gray", interpolation="lanczos")

        plt.show(True)

if show_signs == True:
    # visualize original dataset
    visualize_dataset(X_train, class_indexes_train, imgs_per_class=10)

Step 2: Design and Test a Model Architecture

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!

With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.

There are various aspects to consider when thinking about this problem:

  • Neural network architecture (is the network over or underfitting?)
  • Play around preprocessing techniques (normalization, rgb to grayscale, etc)
  • Number of examples per label (some have more than others).
  • Generate fake data.

Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.

Pre-process the Data Set (normalization, grayscale, etc.)

Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, (pixel - 128)/ 128 is a quick way to approximately normalize the data and can be used in this project.

Other pre-processing steps are optional. You can try different techniques to see if it improves performance.

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.

In [5]:
def equalize_dataset(data, colorspace_conversion=None):
    adapthist_clipLimit =  0.05    # range: [0.0 - 1.0]
    adapthist_grid_size =  [8, 8]  # kernel size in pixels
    
    clahe_clipLimit = 5
    clahe_grid_size = (5,5)
    clahe = cv2.createCLAHE(clipLimit=clahe_clipLimit, tileGridSize=clahe_grid_size)
    
    if colorspace == "BW":
        data_norm = np.zeros((data.shape[0], data.shape[1], data.shape[2], 1))
    else:
        data_norm = np.copy(data)

    if colorspace_conversion == "RGB":
        print("no equalization for RGB images!")
        # no equalization for RGB images, it only makes sense for brightness or luminosity channels!

    elif colorspace_conversion == "YUV":
        for i, img in enumerate(data):
            data_norm[i] = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
            y, u, v = cv2.split(data_norm[i])
            #y = exposure.equalize_adapthist(y, clip_limit=adapthist_clipLimit, kernel_size=adapthist_grid_size)
            y = clahe.apply(y)
            data_norm[i] = np.reshape(cv2.merge((y,u,v)), data_norm.shape[1:])

    elif colorspace_conversion == "LAB":
        for i, img in enumerate(data):
            data_norm[i] = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
            l, a, b = cv2.split(data_norm[i])
            #l = exposure.equalize_adapthist(l, clip_limit=adapthist_clipLimit, kernel_size=adapthist_grid_size)
            l = clahe.apply(l)
            data_norm[i] = np.reshape(cv2.merge((l,a,b)), data_norm.shape[1:])

    elif colorspace_conversion=="BW":
        for i, img in enumerate(data):
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            #img = exposure.equalize_adapthist(img, clip_limit=adapthist_clipLimit, kernel_size=adapthist_grid_size)
            img = clahe.apply(img)
            data_norm[i] = np.reshape(img, data_norm.shape[1:])

    else:
        print("No colorspace conversion set!")
        #data_norm[i] = exposure.equalize_adapthist(data[i], clip_limit=adapthist_clipLimit, kernel_size=adapthist_grid_size)

    return data_norm


def center_normalize(data, mean, std):
    data = data.astype('float32')
    #for i in range(data.shape[0]):
    #    data[i] = (data[i] - data[i].mean()) / (data[i].max() - data[i].min())
    return (data - mean) / std
    #return data
    
X_train_norm = equalize_dataset(X_train, colorspace)
X_valid_norm = equalize_dataset(X_valid, colorspace)
X_test_norm  = equalize_dataset(X_test, colorspace)

X = np.concatenate((X_train, X_valid, X_test))
print("Total dataset mean: %d" % X.flatten().mean())
print("Total dataset std:  %d" % X.flatten().std())

print("Before normalization: min val: %f, max val: %f" % (np.min(X_train_norm), np.max(X_train_norm)))
white_pixels_cnt = len(np.where(X_train == 255)[0])
black_pixels_cnt = len(np.where(X_train == 0)[0])
print("White & black pixels before equalization: %d, %d" % (white_pixels_cnt, black_pixels_cnt))
white_pixels_cnt = len(np.where(X_train_norm == 255)[0])
black_pixels_cnt = len(np.where(X_train_norm == 0)[0])
print("White & black pixels after equalization:  %d, %d" % (white_pixels_cnt, black_pixels_cnt))

# Value normalization into -1 to +1 range
X_mean = 128
X_std  = 128
X_train_norm = center_normalize(X_train_norm, X_mean, X_std)
X_valid_norm = center_normalize(X_valid_norm, X_mean, X_std)
X_test_norm  = center_normalize(X_test_norm,  X_mean, X_std)
print("Normalized data with mean=%.3f and scale=%.3f" % (X_mean, X_std))
print("After normalization min val: %f, max val: %f"  % (np.min(X_train_norm), np.max(X_train_norm)))

datagen = ImageDataGenerator(
    featurewise_center=False,
    featurewise_std_normalization=False,
    samplewise_center=False,
    samplewise_std_normalization=False,
    rotation_range=30,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=False,
    vertical_flip=False,
    zca_whitening=False,
    zca_epsilon=1e-7,
    shear_range=0.1,
    zoom_range=0.1,
    rescale=None
)

datagen.fit(X_train_norm, augment=True)
Total dataset mean: 82
Total dataset std:  68
Before normalization: min val: 7.000000, max val: 255.000000
White & black pixels before equalization: 3678057, 206
White & black pixels after equalization:  1189640, 0
Normalized data with mean=128.000 and scale=128.000
After normalization min val: -0.945312, max val: 0.992188
In [6]:
# Show 5 random images in original and equalized and normalized form
imshow_cmap="gray"
for i in random.sample(list(range(n_train)), 4):
    plt.figure(figsize=(4,1))
    ax = plt.subplot(1, 2, 1)
    ax.set_axis_off()
    ax.imshow(X_train[i].squeeze(), cmap=None)
    ax = plt.subplot(1, 2, 2)
    ax.set_axis_off()
    print("min val: %f, max val: %f"  % (np.min(X_train_norm[i]), np.max(X_train_norm[i])))
    if colorspace == "YUV":
        img = np.reshape(cv2.cvtColor(X_train_norm[i].squeeze(), cv2.COLOR_YUV2RGB), X_train_norm.shape[1:])
    elif colorspace == "LAB":
        img = np.reshape(cv2.cvtColor(X_train_norm[i].squeeze(), cv2.COLOR_Lab2RGB), X_train_norm.shape[1:])
    else:
        img = X_train_norm[i].squeeze()
    ax.imshow(img, cmap=imshow_cmap)
    plt.show()
min val: -0.335938, max val: 0.992188
min val: -0.781250, max val: 0.992188
min val: -0.695312, max val: 0.992188
min val: -0.375000, max val: 0.992188
In [7]:
# Test with different adaptive histogram equalization parameters
list_size = 10
for sample in [2900]: #random.sample(list(range(n_train)), 4):
    X_norm =X_train[sample][:,:,0]
    #X_norm =  cv2.cvtColor(X_train[sample], cv2.COLOR_BGR2GRAY)
    #X_norm = center_normalize(X_norm, X_mean, X_std)
    plt.figure(figsize=(12,4))
    imshow_cmap="gray"
    #imshow_cmap=None
    ax = plt.subplot(1, list_size, 1)
    ax.set_title("orig", fontdict={'fontsize': 8})
    ax.set_axis_off()
    ax.imshow(X_norm, cmap=imshow_cmap) #, vmin=-1.0, vmax=1.0, cmap="gray", interpolation="lanczos")

    for i in range(1, list_size):
        clipLimit = i
        grid_size = (4,4) #(i+2,i+2)
        img = X_norm.astype("uint8")
        clahe = cv2.createCLAHE(clipLimit=clipLimit, tileGridSize=grid_size)
        img = np.reshape(clahe.apply(img), X_norm.shape)
        #img = exposure.equalize_adapthist(img, clip_limit=clipLimit, kernel_size=grid_size)
        #print("clipLimit: %f, grid_size: %s" % (clipLimit, grid_size))
        ax = plt.subplot(1, list_size, i+1)
        ax.set_title("clipLimit: %.2f\ngridSize: %s" %(clipLimit, grid_size), fontdict={'fontsize': 8})
        ax.set_axis_off()
        ax.imshow(img, cmap=imshow_cmap) #, vmin=-1.0, vmax=1.0, cmap="gray", interpolation="lanczos")

    plt.tight_layout()
    plt.show(True)
In [8]:
# show preprocessed images
if True: #show_signs == True:
    X_train_batch, y_train_batch = datagen.flow(X_train_norm, y_train, batch_size=5000, shuffle=True).next()
    indexes  = []
    for i in range(n_classes):
        indexes.append(np.where(y_train_batch==i))

    print("X_train_batch.shape: ", X_train_batch.shape)
    visualize_dataset(X_train_batch, indexes, 10)
X_train_batch.shape:  (5000, 32, 32, 1)
min val: -0.859375, max val: 0.992188
mean: 0.362980, std: 0.392786
Showing example images from class 0: Speed limit (20km/h)
Showing example images from class 1: Speed limit (30km/h)
Showing example images from class 2: Speed limit (50km/h)
Showing example images from class 3: Speed limit (60km/h)
Showing example images from class 4: Speed limit (70km/h)
Showing example images from class 5: Speed limit (80km/h)
Showing example images from class 6: End of speed limit (80km/h)
Showing example images from class 7: Speed limit (100km/h)
Showing example images from class 8: Speed limit (120km/h)
Showing example images from class 9: No passing
Showing example images from class 10: No passing for vehicles over 3.5 metric tons
Showing example images from class 11: Right-of-way at the next intersection
Showing example images from class 12: Priority road
Showing example images from class 13: Yield
Showing example images from class 14: Stop
Showing example images from class 15: No vehicles
Showing example images from class 16: Vehicles over 3.5 metric tons prohibited
Showing example images from class 17: No entry
Showing example images from class 18: General caution
Showing example images from class 19: Dangerous curve to the left
Showing example images from class 20: Dangerous curve to the right
Showing example images from class 21: Double curve
Showing example images from class 22: Bumpy road
Showing example images from class 23: Slippery road
Showing example images from class 24: Road narrows on the right
Showing example images from class 25: Road work
Showing example images from class 26: Traffic signals
Showing example images from class 27: Pedestrians
Showing example images from class 28: Children crossing
Showing example images from class 29: Bicycles crossing
Showing example images from class 30: Beware of ice/snow
Showing example images from class 31: Wild animals crossing
Showing example images from class 32: End of all speed and passing limits
Showing example images from class 33: Turn right ahead
Showing example images from class 34: Turn left ahead
Showing example images from class 35: Ahead only
Showing example images from class 36: Go straight or right
Showing example images from class 37: Go straight or left
Showing example images from class 38: Keep right
Showing example images from class 39: Keep left
Showing example images from class 40: Roundabout mandatory
Showing example images from class 41: End of no passing
Showing example images from class 42: End of no passing by vehicles over 3.5 metric tons

Balance dataset (equal amount of training samples for each class)

In [8]:
# first find class with least exmpales
import itertools

samples = []
for class_nr in range(n_classes):
    samples.append(len(class_indexes_train[class_nr][0]))

class_min_size = np.argmin(np.array(samples))
class_max_size = np.argmax(np.array(samples))
min_size = len(class_indexes_train[class_min_size][0])
max_size = len(class_indexes_train[class_max_size][0])
print("min_size: %d, max_size: %d" % (min_size, max_size))
balanced_index = []
for class_nr in range(n_classes):
    #balanced_index.append(random.sample(list(class_indexes_train[class_nr][0]), min_size))
    size = len(class_indexes_train[class_nr][0])
    int_multiple = max_size // size
    rest = max_size % size
    #print("Class %d: size=%d, int_multiple=%d, rest=%d" %(class_nr, size, int_multiple, rest))
    for i in range(int_multiple):
        balanced_index.append(class_indexes_train[class_nr][0])
    balanced_index.append(random.sample(list(class_indexes_train[class_nr][0]), rest))

balanced_index = np.array(list(itertools.chain.from_iterable(balanced_index)))
print("final size: %d" % balanced_index.shape)
print("size should be %d" % (max_size * n_classes))
min_size: 180, max_size: 2010
final size: 86430
size should be 86430

Model Architecture

In [45]:
# Select model globally for training, validation and testing
model = "LeNet2"

# clean-up tensorflow before we build up the graph
tf.reset_default_graph()

# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1

# really make sure it gets the dimensions right!
if colorspace == "BW":
    image_shape = (image_shape[0], image_shape[1], 1)

def maxpool2d(x, k=2, s=2, padding='VALID'):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, s, s, 1], padding=padding)

def conv2d(x, W, b, strides=1, padding='VALID'):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding=padding)
    x = tf.nn.bias_add(x, b)
    return tf.nn.elu(x)

#new_height = (input_height - filter_height + 2 * P)/S + 1

# Layers weights & biases for LeNet1
weights1 = {
    'wc1':     tf.Variable(tf.random_normal([5, 5, image_shape[2],  6], mean=mu, stddev=sigma)),
    'wc2':     tf.Variable(tf.random_normal([5, 5,  6, 16], mean=mu, stddev=sigma)),
    'wfull1':  tf.Variable(tf.random_normal([400, 120], mean=mu, stddev=sigma)),
    'wfull2':  tf.Variable(tf.random_normal([120,  84], mean=mu, stddev=sigma)),
    'out':     tf.Variable(tf.random_normal([84,  n_classes], mean=mu, stddev=sigma))
}

biases1 = {
    'bc1':     tf.Variable(tf.random_normal([6], mean=mu, stddev=sigma)),
    'bc2':     tf.Variable(tf.random_normal([16], mean=mu, stddev=sigma)),
    'bfull1':  tf.Variable(tf.random_normal([120], mean=mu, stddev=sigma)),
    'bfull2':  tf.Variable(tf.random_normal([84], mean=mu, stddev=sigma)),
    'out':     tf.Variable(tf.random_normal([n_classes], mean=mu, stddev=sigma))
}

def LeNet1(x, keep_prob):    
    # Layer 1: Convolutional. Input = 32x32ximage_shape[2]. Output = 28x28x6.
    conv1 = conv2d(x, weights1['wc1'], biases1['bc1'], strides=1, padding='VALID')
    # Pooling. Input = 28x28x6. Output = 14x14x6.
    conv1 = maxpool2d(conv1, k=2, s=2)
    
    # Layer 2: Convolutional. Output = 10x10x16.
    conv2 = conv2d(conv1, weights1['wc2'], biases1['bc2'], padding='VALID')
    # Pooling. Input = 10x10x16. Output = 5x5x16.
    conv2 = maxpool2d(conv2, k=2, s=2)
    # Flatten. Input = 5x5x16. Output = 400.
    flat = flatten(conv2)

    # Layer 3: Fully Connected. Input = 400. Output = 120.
    layer3 = tf.add(tf.matmul(flat, weights1['wfull1']), biases1['bfull1'])
    # Activation.
    layer3 = tf.nn.elu(layer3)
    # Dropout
    layer3 = tf.nn.dropout(layer3, keep_prob)
    
    # Layer 4: Fully Connected. Input = 120. Output = 84.
    layer4 = tf.add(tf.matmul(layer3, weights1['wfull2']), biases1['bfull2'])
    # Activation.
    layer4 = tf.nn.elu(layer4)
    # Dropout
    layer4 = tf.nn.dropout(layer4, keep_prob)
    
    # Layer 5: Fully Connected. Input = 84. Output = n_classes.
    logits = tf.add(tf.matmul(layer4, weights1['out']), biases1['out'])

    net = {"conv1": conv1, "conv2": conv2, "layer3": layer3, "layer4": layer4, "logits": logits}
    return net



# Layers weights & biases for LeNet2
weights2 = {
    'wc1':     tf.Variable(tf.random_normal([5, 5, image_shape[2],  32], mean=mu, stddev=sigma)),
    'wc2':     tf.Variable(tf.random_normal([5, 5, 32, 64], mean=mu, stddev=sigma)),
    'wc3':     tf.Variable(tf.random_normal([5, 5, 64, 128], mean=mu, stddev=sigma)),
    'wfull1':  tf.Variable(tf.random_normal([2048, 400], mean=mu, stddev=sigma)),
    'wfull2':  tf.Variable(tf.random_normal([400,  200], mean=mu, stddev=sigma)),
    'out':     tf.Variable(tf.random_normal([200,  n_classes], mean=mu, stddev=sigma))
}

biases2 = {
    'bc1':     tf.Variable(tf.random_normal([32], mean=mu, stddev=sigma)),
    'bc2':     tf.Variable(tf.random_normal([64], mean=mu, stddev=sigma)),
    'bc3':     tf.Variable(tf.random_normal([128], mean=mu, stddev=sigma)),
    'bfull1':  tf.Variable(tf.random_normal([400], mean=mu, stddev=sigma)),
    'bfull2':  tf.Variable(tf.random_normal([200], mean=mu, stddev=sigma)),
    'out':     tf.Variable(tf.random_normal([n_classes], mean=mu, stddev=sigma))
}

def LeNet2(x, keep_prob):
    # Layer 1: Convolutional. Input = 32x32ximage_shape[2]. Output = 32x32x32.
    conv1 = conv2d(x, weights2['wc1'], biases2['bc1'], strides=1, padding='SAME')
    # Pooling. Input = 32x32x32. Output = 16x16x32.
    conv1 = maxpool2d(conv1, k=2, s=2, padding='SAME')
    
    # Layer 2: Convolutional. Output = 16x16x64.
    conv2 = conv2d(conv1, weights2['wc2'], biases2['bc2'], padding='SAME')
    # Pooling. Input = 16x16x64. Output = 8x8x64.
    conv2 = maxpool2d(conv2, k=2, s=2, padding='SAME')
    
    # Layer 3: Convolutional. Output = 8x8x128.
    conv3 = conv2d(conv2, weights2['wc3'], biases2['bc3'], padding='SAME')
    # Pooling. Input = 8x8x128. Output = 4x4x128.
    conv3 = maxpool2d(conv3, k=2, s=2, padding='SAME')
    # Flatten. Input = 4x4x128. Output = 400.
    flat = flatten(conv3)

    # Layer 4: Fully Connected. Input = 2048. Output = 400.
    layer4 = tf.add(tf.matmul(flat, weights2['wfull1']), biases2['bfull1'])
    # Activation.
    layer4 = tf.nn.elu(layer4) 
    # Dropout
    layer4 = tf.nn.dropout(layer4, keep_prob)
    
    # Layer 5: Fully Connected. Input = 400. Output = 200.
    layer5 = tf.add(tf.matmul(layer4, weights2['wfull2']), biases2['bfull2'])
    # Activation.
    layer5 = tf.nn.elu(layer5)
    # Dropout
    layer5 = tf.nn.dropout(layer5, keep_prob)

    # Layer 6: Fully Connected. Input = 200. Output = n_classes.
    logits = tf.add(tf.matmul(layer5, weights2['out']), biases2['out'])

    net = {"conv1": conv1, "conv2": conv2, "conv3": conv3, "layer4": layer4, "layer5": layer5, "logits": logits}
    return net


# Variables and placeholders
x = tf.placeholder(tf.float32, (None, 32, 32, image_shape[2]))
y = tf.placeholder(tf.int32, (None))
keep_prob = tf.placeholder(tf.float32)

# Creating instance of one of the networks
if model == "LeNet1":
    net = LeNet1(x, keep_prob)
    logits = net["logits"]
    BATCH_SIZE = 8192
    save_filename = "./lenet1"
else:
    net = LeNet2(x, keep_prob)
    logits = net["logits"]
    BATCH_SIZE = 2048
    save_filename = "./lenet2"

save_filename += "-" + colorspace


# Tensor definitions
one_hot_y = tf.one_hot(y, n_classes)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer() # ,learning_rate=learning_rate)
training_operation = optimizer.minimize(loss_operation)

# Calculate and report the accuracy on the training and validation set
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver(filename=save_filename)

def evaluate(X_data, y_data):
    num_examples = len(X_data)
    total_accuracy = 0
    sess = tf.get_default_session()
    for offset in range(0, num_examples, BATCH_SIZE):
        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
        total_accuracy += (accuracy * len(batch_x))

    return total_accuracy / num_examples

Train, Validate and Test the Model

A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.

Tensorflow network

In [37]:
# training the model
rate_start = 0.001

dropout = 0.2  # Dropout, probability to keep units
epochs  = 5

biased_index = []
biased_index.append(class_indexes_train[30][0])
biased_index.append(class_indexes_train[27][0])
biased_index = np.array(list(itertools.chain.from_iterable(biased_index)))

sample_selection = balanced_index #range(n_train) #balanced_index #biased_index

print("Model parameters: %d" % np.sum([np.prod(v.shape) for v in tf.trainable_variables()]))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    if tf.train.latest_checkpoint('.'):
        print("Trying to load model and variables from last checkpoint: %s" % tf.train.latest_checkpoint('.'))
        saver.restore(sess, tf.train.latest_checkpoint('.'))
        #saver.restore(sess, save_filename)
    else:
        print("Not checkpoint found, starting training from scratch!")
    
    print("Training...")
    for step in range(epochs):
        t0 = time.time()
        #batch_cnt = n_train//BATCH_SIZE + 1
        batch_cnt = len(sample_selection)//BATCH_SIZE + 1
        batches = 0
        for X_train_batch, y_train_batch in datagen.flow(X_train_norm[sample_selection], y_train[sample_selection], batch_size=BATCH_SIZE, shuffle=True):
            sess.run(training_operation, feed_dict={x: X_train_batch, y: y_train_batch, keep_prob: dropout})
            batches += 1
            if batches > batch_cnt:
                break
        loss = sess.run(loss_operation, feed_dict={x: X_train_batch, y: y_train_batch, keep_prob: 1.0})
        validation_accuracy = evaluate(X_valid_norm, y_valid)
        print("Epoch %d took %.2fs - Loss: %.5f, Validation Accuracy: %.5f" %
                (step + 1, time.time() - t0, loss, validation_accuracy))
        saver.save(sess, save_filename, global_step=step)

    saver.save(sess, save_filename)
    print("Model saved")
Model parameters: 1229978
Trying to load model and variables from last checkpoint: ./lenet2-BW-4
INFO:tensorflow:Restoring parameters from ./lenet2-BW-4
Training...
Epoch 1 took 139.15s - Loss: 0.00777, Validation Accuracy: 0.98231
Epoch 2 took 136.66s - Loss: 0.01230, Validation Accuracy: 0.97982
Epoch 3 took 137.17s - Loss: 0.00932, Validation Accuracy: 0.98254
Epoch 4 took 137.78s - Loss: 0.01122, Validation Accuracy: 0.97937
Epoch 5 took 138.56s - Loss: 0.00543, Validation Accuracy: 0.98073
Model saved

Test with Keras

Setting up a slightly different model with more parameters in keras to learn how the abstraction layer of tensorflow work. Wow, this really is much easier and convenient to set-up...

In [38]:
keras.backend.clear_session()
keras_savename="LeNet3.weights"

if colorspace == "BW":
    image_shape = (image_shape[0], image_shape[1], 1)

def LeNet3_model(keep_prob=0.5):
    # really make sure it gets the dimensions right!

    model = Sequential()
    model.add(Conv2D(64, kernel_size=(3, 3), input_shape=image_shape, activation='elu'))
    model.add(MaxPooling2D((2,2)))
    model.add(Conv2D(128, (3, 3), activation='elu'))
    model.add(MaxPooling2D((2, 2)))
    model.add((Dropout(keep_prob)))
    model.add(Flatten())
    model.add(Dense(256, activation='elu'))
    model.add(Dropout(keep_prob))
    model.add(Dense(43, activation='softmax'))
    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])
    return model

LeNet3 = LeNet3_model(0.2)
try:
    LeNet3.load_weights(keras_savename)
except:
    print("Could not load weights for LeNet3 keras model, starting training from scratch!")

# convert class vectors to binary class matrices
y_train_keras = keras.utils.to_categorical(y_train, n_classes)
y_valid_keras = keras.utils.to_categorical(y_valid, n_classes)
y_test_keras  = keras.utils.to_categorical(y_test, n_classes)

Training model

In [39]:
epochs  = 5
biased_index = []
biased_index.append(class_indexes_train[30][0])
biased_index.append(class_indexes_train[27][0])
biased_index = np.array(list(itertools.chain.from_iterable(biased_index)))

sample_selection = balanced_index #range(n_train) #balanced_index #biased_index
BATCH_SIZE = 4096
LeNet3.summary()
LeNet3.fit_generator(datagen.flow(X_train_norm[sample_selection], y_train_keras[sample_selection], batch_size=BATCH_SIZE),
                     steps_per_epoch=len(sample_selection) // BATCH_SIZE,  epochs=epochs,
                     validation_data=(X_valid_norm, y_valid_keras), verbose=1)

score = LeNet3.evaluate(X_test_norm, y_test_keras, verbose=1)
print("Test loss:    ", score[0])
print("Test accuracy:", score[1])
LeNet3.save_weights(keras_savename)
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 30, 30, 64)        640       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 13, 13, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 128)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 6, 6, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 4608)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 256)               1179904   
_________________________________________________________________
dropout_2 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 43)                11051     
=================================================================
Total params: 1,265,451
Trainable params: 1,265,451
Non-trainable params: 0
_________________________________________________________________
Epoch 1/5
21/21 [==============================] - 73s - loss: 0.0095 - acc: 0.9968 - val_loss: 0.1464 - val_acc: 0.9689
Epoch 2/5
21/21 [==============================] - 70s - loss: 0.0104 - acc: 0.9967 - val_loss: 0.1511 - val_acc: 0.9701
Epoch 3/5
21/21 [==============================] - 70s - loss: 0.0102 - acc: 0.9967 - val_loss: 0.1537 - val_acc: 0.9705
Epoch 4/5
21/21 [==============================] - 69s - loss: 0.0128 - acc: 0.9957 - val_loss: 0.1393 - val_acc: 0.9732
Epoch 5/5
21/21 [==============================] - 69s - loss: 0.0101 - acc: 0.9968 - val_loss: 0.1377 - val_acc: 0.9730
12512/12630 [============================>.] - ETA: 0sTest loss:     0.173924473293
Test accuracy: 0.97347585112
In [27]:
# Test on whole test dataset and for each class
accuracies = []
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))
    train_accuracy = evaluate(X_train_norm, y_train)
    valid_accuracy = evaluate(X_valid_norm, y_valid)
    test_accuracy = evaluate(X_test_norm, y_test)
    print("Training Accuracy   = %.3f" % train_accuracy)
    print("Validation Accuracy = %.3f" % valid_accuracy)
    print("Test Accuracy       = %.3f" % test_accuracy)
    for i, sign in enumerate(class_indexes_test):
        accuracies.append(evaluate(X_test_norm[sign[0]], y_test[sign[0]]))

fig = plt.figure(figsize=(11,3))
ax = plt.bar(range(n_classes), accuracies, align="center", width=0.8)
plt.xticks(range(n_classes), range(n_classes))
plt.xlabel("Traffic sign classes")
plt.ylabel("Accuracy on test dataset")
plt.axis("tight")
#fig.savefig("./examples/pred_tf_test1.png", bbox_inches="tight")
plt.show(True)
INFO:tensorflow:Restoring parameters from ./lenet2-BW-4
Training Accuracy   = 0.999
Validation Accuracy = 0.983
Test Accuracy       = 0.974
In [40]:
# Keras test network accuracy
train_accuracy = LeNet3.evaluate(X_train_norm, keras.utils.to_categorical(y_train, n_classes), verbose=0)[1]
valid_accuracy = LeNet3.evaluate(X_valid_norm, keras.utils.to_categorical(y_valid, n_classes), verbose=0)[1]
test_accuracy  = LeNet3.evaluate(X_test_norm, keras.utils.to_categorical(y_test, n_classes), verbose=0)[1]
print("Training Accuracy   = %.3f" % train_accuracy)
print("Validation Accuracy = %.3f" % valid_accuracy)
print("Test Accuracy       = %.3f" % test_accuracy)

accuracies = []
for i, sign in enumerate(class_indexes_test):
    pred = LeNet3.evaluate(X_test_norm[sign[0]], keras.utils.to_categorical(y_test[sign[0]], n_classes), verbose=0)[1]
    accuracies.append(pred)                                   
fig = plt.figure(figsize=(11,3))
plt.bar(range(n_classes), accuracies, align="center", width=0.8)
plt.xticks(range(n_classes), range(n_classes))
plt.xlabel("Traffic sign classes")
plt.ylabel("Accuracy on test dataset")
plt.axis("tight")
#fig.savefig("./examples/pred_tf_test2.png", bbox_inches="tight")
plt.show(True)
Training Accuracy   = 1.000
Validation Accuracy = 0.973
Test Accuracy       = 0.973

Step 3: Test a Model on New Images

To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.

You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.

Load and Output the Images

In [41]:
import os
import matplotlib.image as mpimg
import scipy

test_images = []
test_labels = [22, 27, 14, 25, 25, 12, 23, 27]
img_src_folder = "extra-examples/"
imagefile_list = sorted(os.listdir(img_src_folder))
for count, imagefile in enumerate(imagefile_list):
    image = mpimg.imread(img_src_folder + imagefile)
    img = scipy.misc.imresize(image, (32, 32), interp="lanczos")
    ax = plt.subplot(1, len(imagefile_list), count+1)
    ax.set_title(imagefile, fontdict={'fontsize': 8})
    ax.set_axis_off()
    ax.imshow(img)
    test_images.append(img)
plt.show(True)

# equalize and normalize data
test_images_norm = equalize_dataset(np.array(test_images), colorspace)
test_images_norm = center_normalize(test_images_norm, X_mean, X_std)

Predict the Sign Type and show top 5 Softmax Probabilities For Each Image Found on the Web

In [46]:
### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
nr_top = 5
top  = tf.nn.top_k(tf.nn.softmax(logits), k=nr_top)
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    for i in range(len(test_images_norm)):
        feed = {x: np.expand_dims(test_images_norm[i], axis=0), keep_prob: 1}
        values, indicies = sess.run(top, feed_dict=feed)
        fig1 = plt.figure(figsize=(4,3))
        gs = gridspec.GridSpec(4,3) 
        ax = plt.subplot2grid((4,3), (0,2))
        ax.imshow(test_images[i].squeeze())
        ax.set_axis_off()
        ax = plt.subplot2grid((4,3), (1,2))
        ax.imshow(test_images_norm[i].squeeze(), cmap="gray")
        ax.set_axis_off()
        plt.subplot2grid((4,3), (0,0), colspan=2, rowspan=2)
        plt.title("%s detection of '%s' sign (class %d)!" %
                  ("Correct" if indicies.flatten()[0] == test_labels[i] else "Wrong",
                   labels[test_labels[i]], test_labels[i]), loc="right")
        
        plt.barh(range(5), values.flatten(), align="center")
        plt.yticks(range(5), ['{:>45}'.format(labels[i]) for i in indicies.flatten()])
        plt.xlabel("Prediction probability")
        plt.xlim(0, 1)
        #fig1.savefig("./examples/pred_tf%d.png" % i, bbox_inches="tight")
        plt.show(True)
INFO:tensorflow:Restoring parameters from ./lenet2-BW

Analyze Performance

In [145]:
### Calculate the accuracy for these new images. 
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))
    test_accuracy = evaluate(test_images_norm, test_labels)
    print("Test image accuracy = %.3f" % test_accuracy)
INFO:tensorflow:Restoring parameters from ./lenet2-BW-1
Test image accuracy = 0.750

Predicting the Sign Type with Keras model

Because of the higher amount of features captured by the keras test model I expect a slightly higher accuracy.

In [42]:
nr_top = 5
top = tf.nn.top_k(LeNet3.predict(test_images_norm), k=nr_top)
with tf.Session() as sess:
    tops = sess.run(top)
    for i in range(len(test_images_norm)):
        values, indicies = tops[0][i], tops[1][i]
        fig1 = plt.figure(figsize=(4,3))
        gs = gridspec.GridSpec(4,3) 
        ax = plt.subplot2grid((4,3), (0,2))
        ax.imshow(test_images[i].squeeze())
        ax.set_axis_off()
        ax = plt.subplot2grid((4,3), (1,2))
        ax.imshow(test_images_norm[i].squeeze(), cmap="gray")
        ax.set_axis_off()
        plt.subplot2grid((4,3), (0,0), colspan=2, rowspan=2)
        plt.title("%s detection of '%s' sign (class %d)!" %
                  ("Correct" if indicies.flatten()[0] == test_labels[i] else "Wrong",
                   labels[test_labels[i]], test_labels[i]), loc="right")

        plt.barh(range(5), values.flatten(), align="center")
        plt.yticks(range(5), ['{:>45}'.format(labels[i]) for i in indicies.flatten()])
        plt.xlabel("Prediction probability")
        plt.xlim(0, 1)
        plt.show(True)

Analyze Performance of keras test model

In [44]:
# Keras test network accuracy
test_accuracy = LeNet3.evaluate(test_images_norm, keras.utils.to_categorical(test_labels, n_classes), verbose=0)[1]
print("Test Accuracy = %.3f" % test_accuracy)
Test Accuracy = 0.875

Step 4 (Optional): Visualize the Neural Network's State with Test Images

This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.

Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the LeNet lab's feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.

For an example of what feature map outputs look like, check out NVIDIA's results in their paper End-to-End Deep Learning for Self-Driving Cars in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.

Combined Image

Your output should look something like this (above)

Visualize your network's feature maps here.

Feel free to use as many code cells as needed.

In [182]:
# image_input: the test image being fed into the network to produce the feature maps
# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer
# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output
# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry

def outputFeatureMap(image_input, tf_activations, activation_name=None, activation_min=-1, activation_max=1 ,plt_num=1):
    # Here make sure to preprocess your image_input in a way your network expects
    # with size, normalization, ect if needed
    # equalize and normalize data
    #image_input = equalize_dataset(image_input, colorspace)
    #image_input = center_normalize(image_input, X_mean, X_std)

    if activation_name not in tf_activations:
        print("Activation layer not found in given tf_activation list!")
        return -1
    
    activation = tf_activations[activation_name].eval(session=sess, feed_dict={x: image_input})
    featuremaps = activation.shape[3]
    plt.figure(plt_num, figsize=(12,10))
    #plt.suptitle("Featuremaps (size: %d)" % featuremaps, fontsize=12)
    print("Layer %s featuremaps (size: %d)" % (activation_name, featuremaps))

    for featuremap in range(featuremaps):
        ax = plt.subplot(featuremaps//8, 8, featuremap+1) # sets the number of feature maps to show on each row and column
        #plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number
        ax.set_title('FeatureMap ' + str(featuremap), fontdict={'fontsize': 8})
        ax.set_axis_off()
        if activation_min != -1 and activation_max != -1:
            ax.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin =activation_min, vmax=activation_max, cmap="gray")
        elif activation_max != -1:
            ax.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmax=activation_max, cmap="gray")
        elif activation_min !=-1:
            ax.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin=activation_min, cmap="gray")
        else:
            ax.imshow(activation[0,:,:, featuremap], interpolation="nearest", cmap="gray")

    plt.tight_layout(h_pad=0.01, w_pad=0.01, pad=0.1)
    plt.show()


with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))
    net = LeNet2(x, keep_prob)
    print("conv1:")
    outputFeatureMap(X_train_norm[class_indexes_train[20][0]], net, "conv1")
    #outputFeatureMap(X_train_norm, net, "conv1")
    print("conv2:")
    outputFeatureMap(X_train_norm[class_indexes_train[20][0]], net, "conv2")
    #outputFeatureMap(X_train_norm, net, "conv2")
INFO:tensorflow:Restoring parameters from ./lenet2-BW-7
conv1:
Layer conv1 featuremaps (size: 32)
conv2:
Layer conv2 featuremaps (size: 64)